DATA COMPRESSION AND ABNORMAL SITUATION DETECTION IN A WIRELESS SENSOR NETWORK

• US 20070030816A1
• Filed: 08/08/2005
• Published: 02/08/2007
• Est. Priority Date: 08/08/2005
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Kolavennu, Soumitri
• Application Number:
  US11/161,568
• Publication Number:
  US 20070030816A1
• Time in Patent Office: Days
• US Class Current: 370/252
• CPC Class Codes: H03M 7/30 Compression speech analysis-synthesis for redundancy reduction G10L19/00; for image communication H04N; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

Wireless communication systems adapted for compressing data prior to certain communications. Data compression may be limited or skipped when it is determined that the data compression may cause an unacceptable amount of data to be lost. Abnormal situation detection as part of data compression is included. Methods associated with such systems are also encompassed.

Claims

1. A wireless communication system comprising a destination node and one or more sensors, wherein:

the sensors gather first data having first dimensions;

second data is generated from the first data, the second data having second dimensions less than the first dimensions;

if the second data is an approximation of the first data within a set of distortion parameters, the second data is transmitted to the destination node;

else the second data is not transmitted to the destination node.

2. The system of claim 1 wherein, if the second data is not an approximation of the first data within a set of distortion parameters, the first data is transmitted to the destination node.

3. The system of claim 1 further comprising infrastructure nodes wherein each sensor generates single dimension data points that are gathered at the infrastructure nodes as the first data.

4. The system of claim 1 wherein certain of the sensors are infrastructure nodes as well, and the infrastructure nodes are used to gather the first data from other sensors and route the second data to the destination node.

5. The system of claim 1 wherein principal components analysis is used to generate a conversion matrix for the first data, and truncation is used to reduce the number of dimensions of the second data.

6. The system of claim 1 wherein each sensor generates multi-dimensional data.

7. The system of claim 6 wherein a sensor gathers the first data, generates the second data from the first data, and determines whether the second data is an approximation of the first data within the set of distortion parameters.

8. The system of claim 1 wherein the system engages in a training mode including the steps of:

gathering a plurality of multi-dimensional data points in the same manner as the first data is gathered, each multi-dimensional data point having parameters in common with the first data;

performing principal components analysis on the plurality of multi-dimensional data points to construct a principal components matrix for transforming the multi-dimensional data points; and

identifying one or more dimensions for truncation of data using the principal components matrix and the distortion parameters.

9. A method of operation within a wireless communication network, the wireless communication network including at least one destination node and one or more sensors, the method comprising:

performing a data transfer function including the following steps;

capturing first data using the sensors, the first data having a number of dimensions;

transforming the first data into second data having a reduced number of dimensions; and

determining whether the second data approximates the first data within a distortion parameter, and;

if so, transmitting the second data with addressing instructions for reaching the destination node.

10. The method of claim 9 wherein the network further includes at least one infrastructure node, wherein an infrastructure node receives data from a plurality of the sensors to construct the first data, and performs the steps of transforming, determining and transmitting.

11. The method of claim 9 wherein the sensors are multi-dimensional sensors and the sensors perform the steps of transforming and determining.

12. The method of claim 9, wherein, if the second data does not approximate the first data within a distortion parameter, the first data is transmitted to the destination node.

13. The method of claim 12 further comprising:

if the second data is transmitted, receiving the second data at the destination node;

or

if the first data is transmitted, receiving the first data at the destination node, noting that the first data was received, and determining whether reconfiguration is needed to modify how the transforming step is performed.

14. The method of claim 13 wherein the step of determining whether reconfiguration is needed includes observing how often first data, rather than second data, is received.

15. The method of claim 13 wherein the transforming step includes using a transformation matrix related to a principal components analysis of data previously captured by the sensors, and if it is determined that reconfiguration is needed, the method includes reconfiguring by recalculating the transformation matrix.

16. The method of claim 9 wherein, if the second data does not approximate the first data within a distortion parameter, an abnormal situation is indicated to the destination node.

17. The method of claim 16 wherein, if an abnormal situation is indicated to the destination node, the destination node determines where the abnormal situation occurred.

18. The method of claim 9 wherein the transforming step includes reducing the number of dimensions by a number M, the method further comprising performing a training function including the following steps:

accumulating a training set including number of multi-dimensional data points related to data captured by the sensors;

analyzing the training set to construct a principal components matrix;

transforming the training set into a principal components set; and

starting with N=1, performing the following steps;

truncating elements of the training set by a number of dimensions, N;

determining whether the truncated elements approximate corresponding multi-dimensional data points to within a training parameter; and

if so, increasing N and going back to the truncating step;

or

if not, setting M equal to N−

1.

19. A wireless communication system comprising a destination node, one or more infrastructure nodes, and a number of sensors, wherein:

an infrastructure node receives first data from the sensors, the first data having a first set of dimensions;

the infrastructure node generates second data from the first data, the second data having a second set of dimensions, the second set of dimensions being reduced from the first set of dimensions;

the infrastructure node determines whether the second data provides an approximation of the first data within a set of parameters; and

;

if so, the infrastructure node directs the second data to the destination node.

20. The system of claim 19 wherein, if the second data does not provide an approximation of the first data within a set of parameters, the infrastructure node directs the first data to the destination node.

21. The system of claim 20 wherein, if reconfiguration is indicated:

the destination node receives a training set comprising multi-dimensional data points captured from the sensors;

a transformation matrix is generated using principal components analysis of the training set;

a dimension reducer is generated using the training set, the transformation matrix, and a parameter for training distortion, the dimension reducer indicating how many dimensions of data may be truncated during the step of generating the second data from the first data; and

the transform matrix and dimension reducer are communicated to the infrastructure node for use in the step of generating the second data from the first data.